Between heat death and drought stress, the impact of adverse environmental conditions on critical development stages of agricultural production in the North German Plain

Changing boundary conditions through environmental shifts, worldwide as well as regional, challenge well- established agricultural production systems. While the extraordinary impacts on crop development through adverse environmental conditions during critical development stages are frequently considered a risk, they are rarely analysed. This is likely due to the complexity of the problem, with interactions and interdependencies between numerous abiotic and biotic factors entangled on various levels. This thesis investigates these complex interactions between adverse environmental conditions and critical development stages and their impact on agricultural production of the North German Plain. It identifies important, critical development stages, it develops an outlook for the abundance of adverse environmental conditions, and it identifies mitigation strategies for this specific problem by pattern analysis. A literature study identifies prominent critical development stages that help navigate the topic of adverse environmental conditions and critical development stages in agricultural production. Further, it shows that crop simulation models seemingly lack in capacities to model development-stage specific stress responses. A modelling study provides an outlook; it finds a consistent increase in abundance of numerous adverse environmental conditions throughout the North German Plain. The inabilities of crop simulation models (DSSAT) are omitted by neglecting modelled yield response and focusing on the evaluation of the abundance of adverse environmental conditions within phenological development stages. A case study of drought impact on yield variability approaches the problem from another angle. The inventory of drought patterns shows that diversification of production systems is a possible mitigation strategy. Further, it found a starting point for improvements of crop simulation models towards a better assessment of critical development stages in the poorly simulated drought response around flowering. This inventory was derived for various production systems for an example region in the North German Plain.Widrige Witterungsbedingungen während kritischer Wachstumsphasen können eine außergewöhnlich starke Wirkung auf die pflanzliche Entwicklung haben, z.B. Trockenheit während der Blüte. Dabei reichen die Auswirkungen von Ertragsrückgängen über Qualitätseinbußen bis zum Totalausfall. Es ist anzunehmen, dass die etablierten Produktionssysteme künftig nicht mehr an die veränderten Umweltbedingungen angepasst sein werden und sich solche Konsequenzen häufen werden. Damit geht das Risiko einher, dass die Produktion nicht mehr auf dem gewohnt hohen und zuverlässigen Niveau stattfinden kann. Dies gilt für die Landwirtschaft im Norddeutschen Tiefland wie weltweit. Um diese Risiken für das Norddeutsche Tiefland im speziellen einzuschätzen, wurde in dieser Arbeit eine Übersicht zu kritischen Phasen der pflanzlichen Entwicklung und Ertragsbildung erstellt, eine Perspektive für Risiken der Landwirtschaft im Norddeutschen Tiefland entwickelt und ein systematischer Ansatz zur Verbesserung von Analysemethoden und Werkzeugen getestet. Kritische Phasen werden schon lange als Herausforderung wahrgenommen. Die Literaturübersicht zeigt, dass je nach Fragestellung zahlreiche spezifische Definitionen genutzt werden, und dass systematische Ansätze zur Analyse der Wirkung von widrigen Witterungsbedingungen auf kritische Phasen selten sind. Zusätzlich wird gezeigt, dass kritische Phasen als Phänologie-spezifische Reaktionen auf bestimmte Umweltbedingungen in Pflanzenwachstumsmodellen, dem Werkzeug der Wahl zur Analyse von Produktionssystemen, kaum entwickelt sind. Mit dem Pflanzenwachstumsmodell DSSAT (Decision Support System for Agricultural Transfer) konnte, trotz der für Pflanzenwachstumsmodelle typischen Beschränkungen, die Häufigkeit von widrigen Witterungsbedingungen während ausgesuchter Pflanzenwachstumsphasen für drei Zukunftsszenarien abgeleitet werden. Unter der Voraussetzung, dass es zu keinerlei Anpassungen kommt, ergeben sich für das Norddeutsche Tiefland folgende Perspektiven: Die Häufigkeiten für widrige Witterungsbedingungen während ausgewählter Wachstumsphasen nimmt durch alle evaluierten Szenarien durchgängig zu und dies trotz vorteilhafter, phänologischer Entwicklungen wie der Verlängerung der Vegetationsperiode. Darüber hinaus fordert der Klimawandel den etablierten Pflanzenbau im Norddeutschen Tiefland teils auch auf unerwartete Weise heraus, so muss trotz Temperaturerhöhung weiterhin mit Spätfrost gerechnet werden. Häufig treten widrige Umweltbedingungen nicht vollständig willkürlich auf. Eine Auswertung langer Ertragszeitreihen durch eine Musteranalyse zeigt und klassifiziert die Wirkung von Trockenheit auf die Ertragsvariabilität in Niedersachsen. Neben der Klassifizierung der rezenten Produktionssysteme, die Schlüsse über eine Risiken-vermindernde Gestaltung von zukünftigen Produktionssystemen geben kann, identifiziert die Anwendung der Methode auf modellierte Ertragsreihen Ansatzpunkte, an denen das Pflanzenwachstumsmodell gezielt mittels Phänologie-spezifischer Prozesse verbessert werden kann, z. B. der verbesserten Simulation des Übergangs zur reproduktiven Entwicklung

Simple process-based simulators for generating spatial patterns of habitat loss and fragmentation: a review and introduction to the G-RaFFe model

Landscape simulators are widely applied in landscape ecology for generating landscape patterns. These models can be divided into two categories: pattern-based models that generate spatial patterns irrespective of the processes that shape them, and process-based models that attempt to generate patterns based on the processes that shape them. The latter often tend toward complexity in an attempt to obtain high predictive precision, but are rarely used for generic or theoretical purposes. Here we show that a simple process-based simulator can generate a variety of spatial patterns including realistic ones, typifying landscapes fragmented by anthropogenic activities. The model ‘‘G-RaFFe’’ generates roads and fields to reproduce the processes in which forests are converted into arable lands. For a selected level of habitat cover, three factors dominate its outcomes: the number of roads (accessibility), maximum field size (accounting for land ownership patterns), and maximum field disconnection (which enables field to be detached from roads). We compared the performance of G-RaFFe to three other models: Simmap (neutral model), Qrule (fractal-based) and Dinamica EGO (with 4 model versions differing in complexity). A PCA-based analysis indicated G-RaFFe and Dinamica version 4 (most complex) to perform best in matching realistic spatial patterns, but an alternative analysis which considers model variability identified GRaFFe and Qrule as performing best. We also found model performance to be affected by habitat cover and the actual landuses, the latter reflecting on land ownership patterns. We suggest that simple process-based generators such as G-RaFFe can be used to generate spatial patterns as templates for theoretical analyses, as well as for gaining better understanding of the relation between spatial processes and patterns. We suggest caution in applying neutral or fractal-based approaches, since spatial patterns that typify anthropogenic landscapes are often non-fractal in nature.Fil: Pe’er, Guy. Helmholtz Centre for Environmental Research; AlemaniaFil: Zurita, Gustavo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Universidad Nacional de Misiones. Instituto de Biología Subtropical; ArgentinaFil: Schober, Lucía. Helmholtz Centre for Environmental Research; AlemaniaFil: Bellocq, Maria Isabel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ecología, Genética y Evolución; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Strer, Maximilian. Helmholtz Centre for Environmental Research; AlemaniaFil: Muller, Michael. Helmholtz Centre for Environmental Research; AlemaniaFil: Putz, Sandro. Helmholtz Centre for Environmental Research; Alemani

Abundance of adverse environmental conditions during critical stages of crop production in Northern Germany

BACKGROUND: Understanding the abundance of adverse environmental conditions e.g. frost, drought, and heat during critical crop growth stages, which are assumed to be altered by climate change, is crucial for an accurate risk assessment for cropping systems. While a lengthening of the vegetation period may be beneficial, higher frequencies of heat or frost events and drought spells are generally regarded as harmful. The objective of the present study was to quantify shifts in maize and wheat phenology and the occurrence of adverse environmental conditions during critical growth stages for four regions located in the North German Plain. First, a statistical analysis of phenological development was conducted based on recent data (1981–2010). Next, these data were used to calibrate the DSSAT-CERES wheat and maize models, which were then used to run three climate projections representing the maximum, intermediate and minimum courses of climate development within the RCP 8.5 continuum during the years 2021–2050. By means of model simulation runs and statistical analysis, the climate data were evaluated for the abundance of adverse environmental conditions during critical development stages, i.e. the stages of early crop development, anthesis, sowing and harvest. RESULTS: Proxies for adverse environmental conditions included thresholds of low and high temperatures as well as soil moisture. The comparison of the baseline climate and future climate projections showed a significant increase in the abundance of adverse environmental conditions during critical growth stages in the future. The lengthening of the vegetation period in spring did not compensate for the increased abundance of high temperatures, e.g. during anthesis. CONCLUSIONS: The results of this study indicate the need to develop adaptation strategies, such as implementing changes in cropping calendars. An increase in frost risk during early development, however, reveals the limited feasibility of early sowing as a mitigation strategy. In addition, the abundance of low soil water contents that hamper important production processes such as sowing and harvest were found to increase locally

Rainfed winter wheat cultivation in the North German Plain will be water limited under climate change until 2070

Background: We analysed regionalised ECHAM6 climate data for the North German Plains (NGP) in two time slots from 1981 to 2010 and 2041 to 2070. - Results: The annual mean temperature will increase significantly (by about 2 °C) that will result in shorter growing periods since the sum of degree days until harvest will be reached earlier. Even if the amount of total precipitation does not change there appears to be a shift towards increased winter precipitation and thus noticeable reduced summer precipitation. - Conclusions: Through the example of winter wheat we show a future limitation of water availability if yields are to be maintained or even increase

MOESM1 of Abundance of adverse environmental conditions during critical stages of crop production in Northern Germany

Additional file 1. Suppl Materials 1–4

MOESM2 of Abundance of adverse environmental conditions during critical stages of crop production in Northern Germany

Additional file 2. Suppl Materials 5–12

Strength of effect of <i>G-RaFFe</i>'s main parameters on landscape attributes.

<p>Multiple regression analysis and partial regression analysis between model parameters (number of roads, field size and maximum field disconnection) and a) the Number of patches, b) Average patch size, c) Largest Patch Index (LPI), d) Euclidian distance between patches, e) Landscape Shape Index (LSI), and f) patch cohesion.</p

Examples of eight real landscape maps compared to corresponding landscapes generated by <i>G-RaFFe</i>.

<p>Depicted maps have a forest cover of 5, 10, 19, 26, 27, 51, 65, and 90%. Each landscape is compared to a corresponding landscape map generated by <i>G-RaFFe</i> with the parameters that were identified to provide the best match. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0064968#pone-0064968-g008" target="_blank">Figure 8e</a> (27% FC) was matched by <i>G-RaFFe</i> for only 5 metrics, and 3 for the other models; d,e,g,h were poorly matched by <i>Qrule</i> (<5 metrics matched), while <i>Simmap</i> failed for maps a,b,e,f (matching = 0) and g. For maps d,g, and h <i>Simmap</i> performed better than <i>Qrule</i>. For the parameters of the real and virtual landscapes see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0064968#pone.0064968.s002" target="_blank">Appendix S2</a>.</p

Results of Multiple Regression to assess sources of model variability in <i>G-RaFFe</i>.

<p>We depict the parameter coefficients of the acting parameters Road, Field Size and Maximum Field Disconnection (F.Dis.) as well as the direction and strength of effect of the two most important factors, namely forest cover and the number of roads.</p>*<p> = P<0.05.</p

Attributes of the landscapes generated by <i>G-RaFFe</i>.

<p>Generated landscape parameters are illustrated in colored circles, and compared to 50 real landscape maps (full triangles) according to six explored landscape attributes, each against habitat cover: a) Number of patches, b) Average patch size, c) Largest patch index, d) Average distance between patches, e) Landscape Shape Index, and (f) Patch cohesion. The colors represent the effect of the number of roads, expressed by the combination of parameters (<i>a,b</i>) that determines the relation between habitat cover and the number of roads. Overlaps between parameter outputs cannot be seen due to color dominance.</p